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Information
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UAVs for Smart Cities: Protocols, Applications, and Challenges
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UAVs for Smart Cities: Protocols, Applications, and Challenges

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information and Communications Technology".

Deadline for manuscript submissions: closed (31 March 2021) | Viewed by 20108

Special Issue Editors

Dr. Hakim Ghazzai

E-Mail Website
Guest Editor
Stevens Institute of Technology, Hoboken, NJ, USA
Interests: unmanned aerial vehicles; smart cities; Internet of things; wireless communications; intelligent transportation systems; artificial intelligence; vehicle routing problems
Dr. Mohamed Grissa

E-Mail Website
Guest Editor
Circle Internet Financial, Boston, MA, USA
Interests: networks security and privacy; blockchain; spectrum sharing systems; wireless networks; location-based services

Special Issue Information

Dear Colleagues,

Unmanned aerial vehicles (UAVs), also known as drones, are taking different forms, such as helicopters, quadcopters, fixed-wing planes, blimps, and balloons. They have gained a lot of popularity among hobbyists, governments, and industry alike thanks to their mobility, autonomous operation, low acquisition and maintenance cost, and ease of deployment even in remote locations.

UAVs have rapidly developed, and more than ever before, they play a key role in a panoply of smart city applications and services that span photography, real-time monitoring, disaster relief and management, security and surveillance, civil infrastructure inspection, and delivery of goods, to name a few. Some of these applications may require gathering and processing a tremendous amount of data, which could be challenging given the resource-constrained nature of these flying units and may necessitate offloading some of the processing. Moreover, for some of the applications, high-precision positioning and navigation of the UAVs is crucial, which may require solving complex UAV routing problems. Given the unprecedented rate at which this technology is being adopted, there is an urgent need to address these challenges.

This Special Issue aims to share the progress and efforts being made by researchers, practitioners, and regulators towards the information science and technology, data, and communication support for the use of UAVs in smart city applications. This call solicits novel concepts that are currently being pursued or transformative ideas envisioned for the future of UAVs and smart cities. Original, high-quality, unpublished submissions that discuss research, development, and evaluation strategies that support UAVs and their applications are encouraged within the following scope or related areas.

  • Positioning and path optimization problems
  • Collision avoidance and swarming challenges
  • Data delivery, routing, and collection in flying ad hoc networks (FANET)
  • Mobile Internet of Things and Internet of Drones
  • Targets localization using UAVs
  • Information and communication reliability in UAVs
  • Big data processing
  • Fog/Edge computing and UAVs
  • Onboard data storage and transmission
  • Machine learning and AI for UAVs
  • UAV-based IoT applications and services
  • Mission planning and dynamic real-time re-tasking of UAV swarms
  • UAV experimental results and deployment scenarios
  • Data Security and privacy in UAVs
  • Blockchain for UAVs

Dr. Hakim Ghazzai
Dr. Mohamed Grissa
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • unmanned aerial vehicles
  • smart cities
  • Internet of things
  • information and communication technology
  • security

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Published Papers (5 papers)

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Research

17 pages, 2741 KiB  
Article
A Bioinspired Neural Network-Based Approach for Cooperative Coverage Planning of UAVs
by Simone Godio, Stefano Primatesta, Giorgio Guglieri and Fabio Dovis
Information 2021, 12(2), 51; https://doi.org/10.3390/info12020051 - 25 Jan 2021
Cited by 14 | Viewed by 2832
Abstract
This paper describes a bioinspired neural-network-based approach to solve a coverage planning problem for a fleet of unmanned aerial vehicles exploring critical areas. The main goal is to fully cover the map, maintaining a uniform distribution of the fleet on the map, and [...] Read more.
This paper describes a bioinspired neural-network-based approach to solve a coverage planning problem for a fleet of unmanned aerial vehicles exploring critical areas. The main goal is to fully cover the map, maintaining a uniform distribution of the fleet on the map, and avoiding collisions between vehicles and other obstacles. This specific task is suitable for surveillance applications, where the uniform distribution of the fleet in the map permits them to reach any position on the map as fast as possible in emergency scenarios. To solve this problem, a bioinspired neural network structure is adopted. Specifically, the neural network consists of a grid of neurons, where each neuron has a local cost and has a local connection only with neighbor neurons. The cost of each neuron influences the cost of its neighbors, generating an attractive contribution to unvisited neurons. We introduce several controls and precautions to minimize the risk of collisions and optimize coverage planning. Then, preliminary simulations are performed in different scenarios by testing the algorithm in four maps and with fleets consisting of 3 to 10 vehicles. Results confirm the ability of the proposed approach to manage and coordinate the fleet providing the full coverage of the map in every tested scenario, avoiding collisions between vehicles, and uniformly distributing the fleet on the map. Full article
(This article belongs to the Special Issue UAVs for Smart Cities: Protocols, Applications, and Challenges)
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11 pages, 1805 KiB  
Article
Discriminant Analysis of Voice Commands in the Presence of an Unmanned Aerial Vehicle
by Marzena Mięsikowska
Information 2021, 12(1), 23; https://doi.org/10.3390/info12010023 - 8 Jan 2021
Cited by 4 | Viewed by 2467
Abstract
The aim of this study was to perform discriminant analysis of voice commands in the presence of an unmanned aerial vehicle equipped with four rotating propellers, as well as to obtain background sound levels and speech intelligibility. The measurements were taken in laboratory [...] Read more.
The aim of this study was to perform discriminant analysis of voice commands in the presence of an unmanned aerial vehicle equipped with four rotating propellers, as well as to obtain background sound levels and speech intelligibility. The measurements were taken in laboratory conditions in the absence of the unmanned aerial vehicle and the presence of the unmanned aerial vehicle. Discriminant analysis of speech commands (left, right, up, down, forward, backward, start, and stop) was performed based on mel-frequency cepstral coefficients. Ten male speakers took part in this experiment. The unmanned aerial vehicle hovered at a height of 1.8 m during the recordings at a distance of 2 m from the speaker and 0.3 m above the measuring equipment. Discriminant analysis based on mel-frequency cepstral coefficients showed promising classification of speech commands equal to 76.2% for male speakers. Evaluated speech intelligibility during recordings and obtained sound levels in the presence of the unmanned aerial vehicle during recordings did not exclude verbal communication with the unmanned aerial vehicle for male speakers. Full article
(This article belongs to the Special Issue UAVs for Smart Cities: Protocols, Applications, and Challenges)
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24 pages, 12563 KiB  
Article
Using UAV Based 3D Modelling to Provide Smart Monitoring of Road Pavement Conditions
by Ronald Roberts, Laura Inzerillo and Gaetano Di Mino
Information 2020, 11(12), 568; https://doi.org/10.3390/info11120568 - 4 Dec 2020
Cited by 36 | Viewed by 5646
Abstract
Road pavements need adequate maintenance to ensure that their conditions are kept in a good state throughout their lifespans. For this to be possible, authorities need efficient and effective databases in place, which have up to date and relevant road condition information. However, [...] Read more.
Road pavements need adequate maintenance to ensure that their conditions are kept in a good state throughout their lifespans. For this to be possible, authorities need efficient and effective databases in place, which have up to date and relevant road condition information. However, obtaining this information can be very difficult and costly and for smart city applications, it is vital. Currently, many authorities make maintenance decisions by assuming road conditions, which leads to poor maintenance plans and strategies. This study explores a pathway to obtain key information on a roadway utilizing drone imagery to replicate the roadway as a 3D model. The study validates this by using structure-from-motion techniques to replicate roads using drone imagery on a real road section. Using 3D models, flexible segmentation strategies are exploited to understand the road conditions and make assessments on the level of degradation of the road. The study presents a practical pipeline to do this, which can be implemented by different authorities, and one, which will provide the authorities with the key information they need. With this information, authorities can make more effective road maintenance decisions without the need for expensive workflows and exploiting smart monitoring of the road structures. Full article
(This article belongs to the Special Issue UAVs for Smart Cities: Protocols, Applications, and Challenges)
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18 pages, 8959 KiB  
Article
Accuracy Assessment of Small Unmanned Aerial Vehicle for Traffic Accident Photogrammetry in the Extreme Operating Conditions of Kuwait
by Abdullah M. Almeshal, Mohammad R. Alenezi and Abdullah K. Alshatti
Information 2020, 11(9), 442; https://doi.org/10.3390/info11090442 - 14 Sep 2020
Cited by 11 | Viewed by 3729
Abstract
This study presents the first accuracy assessment of a low cost small unmanned aerial vehicle (sUAV) in reconstructing three dimensional (3D) models of traffic accidents at extreme operating environments. To date, previous studies have focused on the feasibility of adopting sUAVs in traffic [...] Read more.
This study presents the first accuracy assessment of a low cost small unmanned aerial vehicle (sUAV) in reconstructing three dimensional (3D) models of traffic accidents at extreme operating environments. To date, previous studies have focused on the feasibility of adopting sUAVs in traffic accidents photogrammetry applications as well as the accuracy at normal operating conditions. In this study, 3D models of simulated accident scenes were reconstructed using a low-cost sUAV and cloud-based photogrammetry platform. Several experiments were carried out to evaluate the measurements accuracy at different flight altitudes during high temperature, low light, scattered rain and dusty high wind environments. Quantitative analyses are presented to highlight the precision range of the reconstructed traffic accident 3D model. Reported results range from highly accurate to fairly accurate represented by the root mean squared error (RMSE) range between 0.97 and 4.66 and a mean percentage absolute error (MAPE) between 1.03% and 20.2% at normal and extreme operating conditions, respectively. The findings offer an insight into the robustness and generalizability of UAV-based photogrammetry method for traffic accidents at extreme environments. Full article
(This article belongs to the Special Issue UAVs for Smart Cities: Protocols, Applications, and Challenges)
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19 pages, 6334 KiB  
Article
The Development of a Defect Detection Model from the High-Resolution Images of a Sugarcane Plantation Using an Unmanned Aerial Vehicle
by Bhoomin Tanut and Panomkhawn Riyamongkol
Information 2020, 11(3), 136; https://doi.org/10.3390/info11030136 - 28 Feb 2020
Cited by 8 | Viewed by 3328
Abstract
This article presents a defect detection model of sugarcane plantation images. The objective is to assess the defect areas occurring in the sugarcane plantation before the harvesting seasons. The defect areas in the sugarcane are usually caused by storms and weeds. This defect [...] Read more.
This article presents a defect detection model of sugarcane plantation images. The objective is to assess the defect areas occurring in the sugarcane plantation before the harvesting seasons. The defect areas in the sugarcane are usually caused by storms and weeds. This defect detection algorithm uses high-resolution sugarcane plantations and image processing techniques. The algorithm for defect detection consists of four processes: (1) data collection, (2) image preprocessing, (3) defect detection model creation, and (4) application program creation. For feature extraction, the researchers used image segmentation and convolution filtering by 13 masks together with mean and standard deviation. The feature extraction methods generated 26 features. The K-nearest neighbors algorithm was selected to develop a model for the classification of the sugarcane areas. The color selection method was also chosen to detect defect areas. The results show that the model can recognize and classify the characteristics of the objects in sugarcane plantation images with an accuracy of 96.75%. After the comparison with the expert surveyor’s assessment, the accurate relevance obtained was 92.95%. Therefore, the proposed model can be used as a tool to calculate the percentage of defect areas and solve the problem of evaluating errors of yields in the future. Full article
(This article belongs to the Special Issue UAVs for Smart Cities: Protocols, Applications, and Challenges)
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